class_docstr: This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
  to instantiate an ALBERT model according to the specified arguments, defining the model architecture. Instantiating
  a configuration with the defaults will yield a similar configuration to that of the ALBERT
  [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      vocab_size (`int`, *optional*, defaults to 30000):
          Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`].
      embedding_size (`int`, *optional*, defaults to 128):
          Dimensionality of vocabulary embeddings.
      hidden_size (`int`, *optional*, defaults to 4096):
          Dimensionality of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      num_hidden_groups (`int`, *optional*, defaults to 1):
          Number of groups for the hidden layers, parameters in the same group are shared.
      num_attention_heads (`int`, *optional*, defaults to 64):
          Number of attention heads for each attention layer in the Transformer encoder.
      intermediate_size (`int`, *optional*, defaults to 16384):
          The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
      inner_group_num (`int`, *optional*, defaults to 1):
          The number of inner repetition of attention and ffn.
      hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu_new"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0):
          The dropout ratio for the attention probabilities.
      max_position_embeddings (`int`, *optional*, defaults to 512):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          (e.g., 512 or 1024 or 2048).
      type_vocab_size (`int`, *optional*, defaults to 2):
          The vocabulary size of the `token_type_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`].
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout ratio for attached classifiers.
      position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
          Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
          positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
          [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
          For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
          with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
  
  Examples:
  
  ```python
  >>> from transformers import AlbertConfig, AlbertModel
  
  >>> # Initializing an ALBERT-xxlarge style configuration
  >>> albert_xxlarge_configuration = AlbertConfig()
  
  >>> # Initializing an ALBERT-base style configuration
  >>> albert_base_configuration = AlbertConfig(
  ...     hidden_size=768,
  ...     num_attention_heads=12,
  ...     intermediate_size=3072,
  ... )
  
  >>> # Initializing a model (with random weights) from the ALBERT-base style configuration
  >>> model = AlbertModel(albert_xxlarge_configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      vocab_size=30000,
      embedding_size=128,
      hidden_size=4096,
      num_hidden_layers=12,
      num_hidden_groups=1,
      num_attention_heads=64,
      intermediate_size=16384,
      inner_group_num=1,
      hidden_act="gelu_new",
      hidden_dropout_prob=0,
      attention_probs_dropout_prob=0,
      max_position_embeddings=512,
      type_vocab_size=2,
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      classifier_dropout_prob=0.1,
      position_embedding_type="absolute",
      pad_token_id=0,
      bos_token_id=2,
      eos_token_id=3,
      **kwargs,
  ):
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          **kwargs,
      )
  
      self.vocab_size = vocab_size
      self.embedding_size = embedding_size
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_hidden_groups = num_hidden_groups
      self.num_attention_heads = num_attention_heads
      self.inner_group_num = inner_group_num
      self.hidden_act = hidden_act
      self.intermediate_size = intermediate_size
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.max_position_embeddings = max_position_embeddings
      self.type_vocab_size = type_vocab_size
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.classifier_dropout_prob = classifier_dropout_prob
      self.position_embedding_type = position_embedding_type
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/albert/configuration_albert.py
imports: from collections import OrderedDict
  from typing import Mapping
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
label_desc: Use image node_id 1 to create a new AlbertConfig object from inherited base classes: PretrainedConfig with example: obj = AlbertConfig(vocab_size, embedding_size, hidden_size, num_hidden_layers, num_hidden_groups, num_attention_heads, intermediate_size, inner_group_num, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, classifier_dropout_prob, position_embedding_type, pad_token_id, bos_token_id, eos_token_id)